Exhibit 99.4

   

Near Merger with KludeIn I Acquisition Corp.

Investor Conference Call Transcript

May 19, 2022

Slides 2 - 5: Disclaimers

Hello Everyone: Thank you for taking the time to listen to this very exciting story. Before I begin, let me ask you to refer you to slides 2 through 5, which contain the disclaimers, including forward looking statements, projections and where you will find additional information.

Slide 6: Near Video

On page six, you will see a short video that describes the firm.

Video: Are consumers flying or driving on vacation? Are they staying in hotels, campgrounds or a friend’s home? Are they shopping online, in store or both? Are they eating in or out? Are they resuming their behavior or have they adopted new behavior? Smart strategic decisions are based on the ability to answer key questions around consumer behavior. That’s where Near comes in. For over 10 years, companies around the world have trusted us to provide insightful answers. We are the data storytellers bringing your data to life. Our platform unites the marketers and operational leaders by providing the most accurate, reliable source of data. We are determined to provide actionable insights as we work relentlessly to shape, build and maintain the world’s largest source of intelligence on people, places and products in both the physical and digital space. Ultimately, our vision is to inspire the world to make better decisions and to inspire ourselves to deliver the most trusted privacy-led source of intelligence on people, places and products. We will not rest until we get there.

Slide 7: Our Presenters

I am Narayan Ramachandran, Chairman & CEO of the KludeIn I Acquisition Corp.

Joining us on the call are the management team of Near, the CFO, Rahul Agarwal, the Chief Operating Officer, Gladys Kong, and co-founder, Shobhit Shukla. The main presenter will be founder & CEO of Near, Anil Mathews, who is a serial entrepreneur now building his third company.

Slide 8: Near Investment Highlights

I am thrilled that KludeIn is partnering with Near in this business combination. Near is a privacy-led, data intelligence SaaS platform, that empowers enterprises to create their own enriched data lakes with a mosaic of information on people and places.

Near’s recipe to do this is unique. The finished product is a platform that synthesizes data from multiple silos, that uses English language queries and presents insights in an easily understandable and convenient way for action. Its secret sauce is patented technology that stitches together data from multiple sources and randomizes and anonymizes this data. If you take away one thing from this presentation, it is that the company has built a truly unique product with patented insights that hands control back to enterprises by enriching and providing deeper insights into their data.

   

It is a company with a real business model, real revenues, a short pathway to profitability and real clients, many of whom are in the Fortune 100.

Near has quietly built a significant presence in North America, with nearly two-thirds of their revenues coming from this region. Yet they remain a global company with significant presence in Asia and Europe, reflecting their birth in Asia. Today, Near is a global company with headquarters in Pasadena and offices in Europe, Asia and Australia.

We have conducted a detailed business, legal, product and operational diligence on the Company. We further engaged with Near's customers, employees and management team and came away impressed.

Let me hand this over to Anil to speak about Near.

Slide 9: Corporate Overview

Thank you, Narayan. Before I get started, let me give you some context, so you can better understand the Near opportunity. Enterprises big and small are sitting on huge amounts of data today. Data on their consumers, on their stores, their products and so on, but they're not able to derive meaningful value out of their data.

We believe that there are three key reasons for that. One, enterprise data is disparate and siloed. It's not unified, it's stored in different places, different formats. Number two, this data is primarily of poor quality. There are gaps in them, missing fields, missing information, and so on. And number three, which we all think is a trivial issue, but it isn't, most enterprises have a significant skills gap, because they're not data companies themselves.

Slide 10: What is Near?

And that's where Near comes into picture, we are a global, full stack data intelligence SaaS platform.

Slide 11: What is Near?

Near is a privacy-led, data intelligence platform that enables these enterprises to stitch disparate, siloed data together using our patented technology, enrich it with deeper understanding of consumers, and derive intelligence, which is actionable and measurable. All on a single hosted platform that we license to these enterprises.

Slide 12: Case Studies

Let me take you through two case studies on slide 13 onwards, so you can understand the near offering better.

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Slide 13: How do our Customers use Near?

First, is one of the largest global media companies in the world. Before Near came into the picture, this customer was dealing with multiple digital properties, which is, large apps and websites. Now, the challenge was, if a consumer visits one of their properties and later visits another property, there was no way to identify this was the same user unless they're logging in. And most of them weren't logging in.

They were also heavily reliant on digital world signals, on what these consumers are watching, what they're liking, and what they're reading, to profile them. Which meant, they were not significantly different in the market when it comes to competing with the behemoths. Since Near came into picture, the first thing we did was that we stitched their siloed properties together, using our patented technology and provided them a unique key for each of their consumers.

Then we were able to provide deeper insights on not only these consumer’s behaviors in the digital world, but also in the physical world; in terms of where they live? Where they work? Which brands they prefer in the real world? Or how much time they spend in a grocery store? And so on. All this, we are able to provide them on a hosted platform.

The result: They were able to create more than 3,000 new customer cohorts using this data. Near’s revenue with this customer grew by more than four times over the last three years. And the most important outcome was that we were able to increase their data yield by around 30%. This means actually we were able to increase their revenues on data without the need of actually increasing the user base itself.

Slide 14: How do Customers use Near?

Now, let me take you through the second case study, which is on slide 14, which is one of the largest global commercial real estate companies out there.

Now this customer relied heavily on archaic methodologies before Near came into the picture because they were using click-trackers and census data to analyze foot traffic to their properties. This meant that they had limited ability to wrangle data, which constrained them from deriving insights across these consumer’s behavior. Since Near came into picture, one of the first things we did was bring in data from our data universe, which is our biggest moat, which comprises of around 1.6 billion monthly active user IDs, across 44 countries. We provided this data to this customer and they were able to now look at real-time footfall traffic, to not only their properties, but to their competitors’ properties. They were also able to use Near’s data to analyze where are these people coming from? Where do they go after? How much time they spend in a property?

And this allowed them to address use cases, which wre not addressable before, including, cross shopping behavior, revenue cannibalization forecast, site development analysis, and so on. All this on a hosted platform. They started with one country with us and today, we are working with them across more than 14 countries. And they're analyzing more than 800,000 places across these three continents.

The most important outcome was we were able to increase the rental revenues on average for property owners by around 10%. Effectively, we were able to increase efficiencies within their organization, using our data and platform.

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Slide 15: Market and Product

Now, let me touch upon how do we go to market and how our products fit into two key market categories.

Slide 16: Customer Revenue & ROI

On slide 16 you'll see that there are two buckets here.

The first one is what we call the marketing intelligence bucket. And the second is the operational intelligence bucket. The first bucket is the first case study I touched upon, wherein we are taking our offering to marketers and CMOs of organizations. Primarily for use cases such as customer engagement, segmentation, competitive assessment and targeted marketing.

As for the operational intelligence bucket, we are taking this to chief technology officers, chief information officers, chief digital officers of these enterprises for use cases such as site selection - that is where to open the next store, supply chain optimization, route planning and so on.

Now, if you clearly look at this, these are two key challenges that enterprises are trying to solve for. The first one being, how do we increase revenues? The second one being, how do we increase efficiencies within an organization?

Slide 17: Near’s Products

All this on slide 17, if you see, are achieved by a suite of products. The first one is what we call Carbon. Carbon allows enterprises to stitch and enrich their data.

So the enterprises are sitting on data, but post Carbon, they’re sitting on rich data. They're sitting on intelligent data. Now once they have this data available, then they can choose one of our products between Allspark and Vista, deciding on how they want to act upon this. What is their end goal and what they want to achieve within the organization. Between these two products, between marketing intelligence and operational intelligence, we are sitting on a TAM (total addressable market) of around $23 billion, which is highly untapped and ours to own.

Slide 18: Activation for the Enterprise

If you look at slide 18, it gives you a better picture of how the data flows from the time it comes from the enterprise to our platform and how it goes out to adjacent platforms. So on step one, enterprises would come to us with two kinds of data primarily. It could be data on their consumers, or it could be data on their places, which is predominantly stores, or if you're a bank it could be branches. If you're an auto maker it could be dealerships and so on.

So as an example, a large retailer will communicate that they have access to data on over 20 million consumers. I know “A, B, C” about them. Can you tell me “D, E, F”? Because the moment they walk out of my property, I lose them. And this could be digital or physical properties. So we could provide them deeper insights, both from the digital and the physical world, whether it is demographics, brand affinity, purchase behavior, work location, home location, employment details, and so on. If it is a place they're looking at, we could give them more properties around this place, which is, let's say, in this case, we could be telling them not only properties across their stores, but their competitor stores in terms of visitation, footfall trends, trade area dynamics, pathing - where they come from and where they're going after, dwell time, and so on.

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All this can be then acted upon and measured using our products. Further, pushed into activation platforms such as, Adobe, Salesforce, Google, and Trade Desk. And also visualization platforms such as Carto, Esri, Tableau, which we are all integrated with.

Slide 19: Near’s Customer Base

This allows us to work with more than a hundred enterprise clients globally. Most of which have significant expansion opportunity.

There are eight key verticals that we touch upon today. Some of them are stronger than the others. For example, in travel and tourism, we work with more than 50 major travel destinations globally. If I take NYC &Company as an example, they're using Near’s data to look at who's coming to New York. How many are coming locally, how many are traveling internationally to New York? Where do they stay when they're in New York? Where do they eat when they're in New York? They're able to get all this near real-time on a dashboard.

If you look at retail, restaurants and real estate, which are very strong verticals for us, most of our customers are using our data to analyze foot traffic to their stores and their competitor stores. And looking at where do we open the next store? How does it compare with the competition? What is the catchment area? Do we open the store on this side of the freeway or the other side, based on human movement data. And, we are able to help them, with all these decisions, using our data and platform.

Slide 20: Near’s Key Business Metrics

The proof is in the pudding. If you look at slide 20, we had more than $50 million in ARR last year. We've been growing 60% year over year. One of the strongest metrics that we measure, is the net retention rate, which is 133% for us in 2021. This is very strong coming out of a COVID year. But typically, on average, it is expected to be around 120%.

We used a global consulting firm to help us derive our net promoter scores and other product metrics, which stands very strong at around 81%.

Adjusted gross margin is around 70% and our customer satisfaction rating is at 9.4 out of 10, where typically, most product companies would have 7.3 to 7.4.

Slide 21: Near has Strong Privacy and Compliance Practices

One challenge that we face, is around privacy. This is because we deal with a lot of enterprise data. Early on in our journey, instead of being reactive about it, we decided to be proactive. And so today, we are compliant in all regions that we are present in, including, in Europe with GDPR and in the U.S. with CCPA.

We are also staying ahead of the curve in regions like Australia and Japan, and are working with local regulators there so that when they come up with their own regulations, we are compliant.

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We are also annually audited by leading firms, who are looking at our data and, helping us decide on how do we bring in data? how do we store it? How do we process it and give it out and still stay compliant in all regions that we're present in?

Slide 22: Near Expansion Opportunities

One thing that I want to touch upon is Near’s expansion opportunities and these are immediate expansion opportunities. On slide 22, there are five verticals where we are working with anchor customers, who are helping us create flavors of our product.

For example, with retail and CPG, we are working with a large global multinational consumer goods company in Asia Pacific. They have been so reliant on the behemoths and the walled gardens that now they want to take control of their data. They have reached out to companies like Near to help them create a data lake. And, and our platform is very well suited to do that. Not only Asia Pacific, but globally for them.

Slide 23: The Winning Flywheel

Moving on slide 23 onwards, I want to outline our winning flywheel.

Slide 24: The Data Network Effect 1

If you look at slide 24, like I touched upon earlier, one of our biggest strengths and the reason what we can do, that nobody else can do, is around our data universe. Our data universe comprises of 1.6 billion monthly active user IDs across 44 countries, whose behavior we are analyzing, across 70 million places.

This data universe is exclusive to us. And over the last decade, it is built with varied streams of data.

Slide 25: The Data Network Effect 2

With each additional customer that we sign up, our data universe is becoming more and more intelligent, which allows us to provide better business outcomes within the ecosystem.

Slide 26: The Data Network Effect 3

And then further allows us to retain those customers and acquire more customers. The more customers we get, the better our data gets, the stronger our data gets. This is our moat. This is a reason nobody else can disrupt us.

Slide 27: The Team & Org

The last thing I want to cover before I hand it over to my colleague, is our team and org.

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Slide 28: The Near Team

If you look at slide 28, I'm very proud of our team, which is comprised of entrepreneurs and those who have created companies before to have come together to create this opportunity. Most of us are spread between Bangalore and California with a strong hub in Paris and Sydney.

Slide 29: Near’s Talent

And if you look at slide 29, we are still a tech company, very strong in engineering and science. 60% of our team comprised of engineering and science between these two hubs.

Slide 30: Near’s Global Presence

Slide 30 gives you a global footprint of how are teams are spread across the world. Also, the investors who supported in us and have invested around $130 million to-date. This includes Sequoia Capital and JP Morgan who continue to remain big believers in Near as we go forward in the next phase of our journey.

Slide 31: Near’s Financial Highlights

With that, I would like to hand it over to my colleague Rahul Agarwal, our CFO, who will walk you through the financials.

Slide 32: Near’s Financial Highlights

Thanks Anil. You all have heard how we have built a business over the last decade. I would love to share the numbers, what it has resulted into and what the future holds for us.

Starting with some key financial highlights, we grew 41% year over year, exiting the year at around $50 million of ARR with a 71% gross margin. The NRR is expected to be around 120%, we'll talk about the NRR on future slides. We managed to secure this revenue across 122 clients with an average revenue per client of $353,000 per year.

Slide 33: Financial Profile

Moving on, a deep dive into the financial profile of the company. In 2021, we closed the year with the billed revenue of $46 million. For this current calendar year, we are expecting $62 million of billed revenue, more than doubling it by 2024, growing at a CAGR of 40% plus. And this is in line with what our historical growth has been, highly attractive SaaS revenue.

On the gross profit margin profile, our margin has improved to 71% for 2021. We expect to further improve this gross profit margin, which will stabilize at around 73% by 2024. The improvement in our gross profit margin, coupled with the increase in revenue will take us to profitability on an EBITDA basis, by 2024.

Slide 34: Attractive Economics

I would like to highlight the contribution margin and strong economics in the business. So these numbers are for 2021. For every dollar of revenue that we are generating, 71 cents is the gross profit margin. And if I take the sales and marketing cost, we are left with a contribution margin of 45 cents, which is expected to improve to around 50 to 53 cents in the outer years.

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And so for every dollar of revenue that we are generating more than half of that will be available for the fixed expenses around G&A, product and technology in years to come.

Slide 35: Strong KPIs

Moving on to slide 35, some key underlying KPIs that we are tracking. Anil spoke about the $23 billion TAM that we operate under. The U.S. is our largest market and our revenues pretty much represent the TAM that we operate under. So the U.S. is two-thirds of our revenue followed by Europe and Asia, which contribute the remaining one-third equally.

On the NRR, 2020 and 2021 were COVID impacted years. So you see a higher number in 2021, and that's because of the COVID tailwind recoveries. In 2022, we expect the NRR to stabilize at 120% and we expect to maintain that in outer years going forward.

So if you look at our revenue expectation, the $46 million for 2021 going to $62 million this year, going all the way to $91 million in 2023 on the back of a strong NRR and the pipeline of revenue that we have, we are extremely confident that we'll be able to hit our numbers.

Slide 36: Near Use of Funds

Finally, I would like to highlight the key use of funds here. Four key verticals. You heard about the data moat and SaaS flywheel that we've created, which is our winning mantra. We will continue to invest in the organic growth of the company.

We will earmark funds for expanding in some key sectors, specifically around retail, real estate and media, where we see significant penetration opportunity.

On the product front, we are continuing to file more patents. We are continuing to innovate. We are coming up with newer features on our existing product line and new products built on the core Near platform. So there will be significant product expansion spends.

And finally, you know, we've been there done that. We acquired a couple of companies and have since then successfully integrated them. We are working like a well-oiled machine now, and we'll continue to look for potential targets, which unlock significant synergies specifically on the product and data front. Accordingly, we will earmark funds for inorganic growth.

Slide 37: Transaction Overview

With this, I'll hand it over to Narayan to talk about the transaction.

Slide 38: Transaction Summary

Business Combination Overview

Near will have a proforma fully diluted enterprise value of $753 million.

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Existing Near Shareholders are rolling over 100% of their equity and will retain 68% proforma ownership.

The transaction assumes $268 million in gross proceeds raised, including $172.5 million of cash in trust and up to $95 million in additional equity financing.

Slide 39/40: Compelling Valuation

KludeIn has also undertaken a full and independent valuation review by Kroll (Duff & Phelps). The chosen valuation is well within the range opined by them.

Near’s valuation comps are very attractive.

We have used four broad categories of comparison: BI SaaS, AI, Marketing and High Growth SaaS, both on EV/revenue and on a EV/Rev standardized for growth. The company compares very favorably as depicted on the slides.

Slide 41: Summary

We look forward to completing this merger and transitioning Near to the public markets. We believe strongly, Near will be a responsible and attractive investment destination for public investors.

Thank you for listening.

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